Financial transactions, internet search, and data analysis are all placing increasing demands on databases. SQL, NoSQL, and NewSQL databases have been developed to meet these demands and each offers unique benefits. SQL, NoSQL, and NewSQL databases also rely on different underlying mathematical models. Polystores seek to provide a mechanism to allow applications to transparently achieve the benefits of diverse databases while insulating applications from the details of these databases. Integrating the underlying mathematics of these diverse databases can be an important enabler for polystores as it enables effective reasoning across different databases. Associative arrays provide a common approach for the mathematics of polystores by encompassing the mathematics found in different databases: sets (SQL), graphs (NoSQL), and matrices (NewSQL). Prior work presented the SQL relational model in terms of associative arrays and identified key mathematical properties that are preserved within SQL. This work provides the rigorous mathematical definitions, lemmas, and theorems underlying these properties. Specifically, SQL Relational Algebra deals primarily with relations - multisets of tuples - and operations on and between those relations. These relations can be modeled as associative arrays by treating tuples as non-zero rows in an array. Operations in relational algebra are built as compositions of standard operations on associative arrays which mirror their matrix counterparts. These constructions provide insight into how relational algebra can be handled via array operations. As an example application, the composition of two projection operations is shown to also be a projection, and the projection of a union is shown to be equal to the union of the projections.

This paper collects the SoC (State-of-Charge) changes of electric vehicles and analyzes the battery consumption behavior. For multiple SoC streams generated on the same road, a Hadoop Pig script filters essential information fields out of the vast amount of SoC records, while its user-defined function adds the distance and the time taken from the start point. Next, neural networks are built to trace the SoC dynamics according to both spatial and temporal aspects of SoC records using the R statistics package. A time-combined model reduces the fitting error by 50 %, compared with the distance-only model. The ongoing project keeps accumulating SoC streams and our model will investigate the effect of other parameters and correlate different streams. An accurate model for major roads taken by most trips, will lead to a more efficient estimation of battery consumption for any point-to-point routes.